store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_990370', 'seed': 990370, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[27069, 55275, 44770, 52614, 45792, 13820, 7660, 32194, 26319, 6336, 11882, 36787, 26796, 28287, 55689, 24565, 22324, 3958, 36499, 15693]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7261, 'nll': 1.8856093645095826}, 'chosen_samples': [51204, 28485, 15704, 4151, 32047, 57009, 38864, 15848, 340, 22793], 'chosen_samples_score': ['1.1702508', '1.1747445', '1.1895885', '1.2330506', '1.2174023', '1.1971326', '1.2014165', '1.2066768', '1.1978933', '1.178235']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7134, 'nll': 1.876303744316101}, 'chosen_samples': [181, 20077, 26333, 51310, 11678, 49140, 7192, 49060, 16479, 39564], 'chosen_samples_score': ['1.0592036', '1.1054134', '1.084295', '1.1113342', '1.0989652', '1.0766654', '1.1123356', '1.181351', '1.1735976', '1.1078691']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7468, 'nll': 1.5626398086547852}, 'chosen_samples': [11990, 59674, 10808, 52286, 3739, 41002, 10044, 40489, 23111, 23512], 'chosen_samples_score': ['0.98253936', '0.98396415', '1.0133252', '1.009087', '1.0156596', '1.1294377', '1.0743732', '1.0262403', '1.1017398', '1.0236602']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7897, 'nll': 1.1400090515613557}, 'chosen_samples': [41078, 59468, 3742, 12196, 9614, 47016, 21383, 19276, 25295, 12840], 'chosen_samples_score': ['0.9809325', '0.9896497', '1.0082822', '1.0883323', '1.0643609', '1.0356765', '1.0176657', '0.9863912', '1.0097029', '1.0218619']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8053, 'nll': 1.0027786016464233}, 'chosen_samples': [8691, 10012, 17108, 32080, 30853, 12377, 6140, 8234, 51314, 8676], 'chosen_samples_score': ['0.8751124', '0.8835797', '0.8858223', '0.89090455', '0.9048757', '0.96361107', '0.908969', '0.93479353', '1.023164', '0.9108407']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.828, 'nll': 0.8728083610534668}, 'chosen_samples': [39411, 23041, 12361, 46978, 42308, 51212, 12938, 43042, 826, 3719], 'chosen_samples_score': ['0.75251186', '0.75990164', '0.7608057', '0.77893716', '0.81618303', '0.8212349', '0.790121', '0.77269375', '0.7669856', '0.7760467']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8479, 'nll': 0.8554762780666352}, 'chosen_samples': [34429, 57632, 5013, 49202, 5331, 16279, 56642, 14386, 54801, 6347], 'chosen_samples_score': ['0.9625925', '0.9668637', '0.96398866', '0.96760696', '0.9723399', '1.0340528', '1.0014648', '0.9718094', '1.0184329', '1.0241492']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.815, 'nll': 0.9005945324897766}, 'chosen_samples': [34804, 40334, 19942, 14520, 44040, 40466, 32056, 1239, 24382, 37048], 'chosen_samples_score': ['0.7112863', '0.7170951', '0.7248492', '0.7201928', '0.7318798', '0.732706', '0.7343223', '0.76673484', '0.7574056', '0.7890652']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.825, 'nll': 0.8780667066574097}, 'chosen_samples': [38167, 18727, 21395, 14625, 55612, 34684, 40457, 9242, 17756, 20859], 'chosen_samples_score': ['0.7383681', '0.7450274', '0.74567044', '0.7473389', '0.7492897', '0.7604591', '0.77504426', '0.7500108', '0.7879418', '0.7615057']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8598, 'nll': 0.7946759402751923}, 'chosen_samples': [28455, 26877, 3977, 54542, 50912, 51004, 7798, 31301, 8857, 39146], 'chosen_samples_score': ['0.9050259', '0.91359955', '0.9183915', '0.9187333', '0.977648', '0.936068', '0.9409568', '0.9540498', '0.92790943', '0.9315506']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8925, 'nll': 0.6627829283475876}, 'chosen_samples': [48040, 27678, 34495, 34407, 57718, 8093, 36704, 3331, 53873, 41642], 'chosen_samples_score': ['0.99131906', '0.9922467', '1.0093646', '1.065818', '1.0550582', '1.036264', '1.0045092', '1.0670462', '1.0128274', '1.0079308']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8901, 'nll': 0.6107232123613358}, 'chosen_samples': [11911, 26527, 15798, 38559, 15723, 26034, 35537, 11693, 22083, 13991], 'chosen_samples_score': ['0.8337251', '0.83451647', '0.8667487', '0.8981519', '0.86068434', '0.9154566', '0.84742945', '0.8467996', '0.86728466', '0.84069735']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9102, 'nll': 0.5864237993955612}, 'chosen_samples': [670, 47505, 5630, 30852, 52697, 14373, 52689, 8714, 49567, 57605], 'chosen_samples_score': ['0.9766498', '0.97831327', '0.9801737', '0.9836998', '0.9950726', '0.9951969', '1.0545595', '1.0616934', '1.0974747', '1.0324763']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9201, 'nll': 0.525791123509407}, 'chosen_samples': [26062, 52138, 44095, 49082, 16488, 26444, 29303, 52914, 16572, 33812], 'chosen_samples_score': ['0.9642191', '0.99673325', '0.9688636', '0.986023', '0.996875', '0.99894404', '1.0029839', '1.014384', '1.0320812', '1.0422523']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9162, 'nll': 0.5514527052640915}, 'chosen_samples': [12399, 58308, 14394, 17010, 6710, 45732, 13428, 20171, 54932, 54832], 'chosen_samples_score': ['0.96608645', '0.9717051', '0.98437816', '0.9832207', '0.98951036', '1.0635142', '1.0169687', '1.0701717', '1.0841475', '1.1392514']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9255, 'nll': 0.47573401480913163}, 'chosen_samples': [39818, 2064, 27458, 54954, 19590, 15801, 41949, 20709, 32323, 9490], 'chosen_samples_score': ['0.9051659', '0.90596634', '0.9098243', '0.9109579', '0.9356768', '1.0325267', '0.92906535', '0.959566', '0.9272811', '0.9219491']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.931, 'nll': 0.4498589918017387}, 'chosen_samples': [15948, 24632, 38647, 22139, 3350, 42317, 23490, 42020, 34520, 27085], 'chosen_samples_score': ['0.9750988', '1.013319', '0.99470955', '1.0149562', '1.0470567', '0.98276687', '1.035337', '1.0682732', '1.1051476', '0.98162425']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9418, 'nll': 0.39201480746269224}, 'chosen_samples': [44202, 31252, 37373, 59731, 50317, 5679, 24424, 36126, 11621, 36744], 'chosen_samples_score': ['0.9263222', '0.9359141', '0.94213676', '0.9470749', '0.9570532', '0.99564844', '0.97420794', '1.0136981', '1.0274882', '1.04143']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9373, 'nll': 0.43561863750219343}, 'chosen_samples': [51154, 10690, 39320, 52173, 34946, 6130, 15779, 9180, 22495, 31954], 'chosen_samples_score': ['0.9751352', '1.0048711', '1.0174836', '1.0186543', '1.0455139', '1.0593569', '1.0431886', '1.0675288', '1.0200404', '1.014492']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9391, 'nll': 0.43063630163669586}, 'chosen_samples': [4820, 40046, 50343, 46021, 3960, 4797, 39668, 49200, 34765, 27328], 'chosen_samples_score': ['0.90318185', '0.90755504', '0.91117334', '0.92085046', '0.91177714', '0.91327703', '0.9450519', '0.93186563', '0.958544', '0.9413184']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.938, 'nll': 0.4212566763162613}, 'chosen_samples': [32002, 21353, 19089, 37016, 10202, 7434, 46412, 42799, 15191, 55388], 'chosen_samples_score': ['0.9154707', '0.9370773', '0.9166329', '0.93999124', '0.91858083', '0.95564765', '0.9691004', '1.1490146', '0.96574455', '1.1422753']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9408, 'nll': 0.4119901701807976}, 'chosen_samples': [55042, 13021, 27169, 3056, 16692, 59919, 25210, 43745, 50789, 42112], 'chosen_samples_score': ['0.9775706', '1.0370934', '1.0762111', '0.9997705', '1.0217825', '0.9857429', '1.0334561', '0.99237394', '0.9853729', '0.9894102']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.948, 'nll': 0.39988886564970016}, 'chosen_samples': [22597, 3415, 8887, 43043, 19344, 13259, 11292, 52140, 50461, 42703], 'chosen_samples_score': ['0.9891147', '0.9891585', '0.9892601', '0.9914786', '1.0026149', '1.0178547', '1.1450944', '1.0321381', '1.04989', '1.0641928']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.952, 'nll': 0.3791410356760025}, 'chosen_samples': [46247, 15134, 37696, 2184, 4153, 50091, 17801, 31672, 49107, 21307], 'chosen_samples_score': ['1.0317132', '1.0498564', '1.0492932', '1.0693042', '1.0678465', '1.0323818', '1.1489396', '1.0410179', '1.0483091', '1.1023859']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9566, 'nll': 0.3345435559749603}, 'chosen_samples': [55148, 1075, 49416, 14706, 8761, 34328, 49487, 55606, 32747, 26358], 'chosen_samples_score': ['0.9827869', '0.9838106', '1.0022056', '0.99685967', '1.0340815', '1.0054007', '1.0360194', '1.0368822', '1.0824723', '1.0549048']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9602, 'nll': 0.33713971078395844}, 'chosen_samples': [24587, 18682, 22480, 34406, 32880, 34846, 5052, 46698, 32926, 24040], 'chosen_samples_score': ['0.99824375', '1.001752', '1.050693', '1.0686983', '1.0261065', '1.0195006', '1.0080138', '1.0041851', '1.0340493', '1.0675733']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9578, 'nll': 0.35009164214134214}, 'chosen_samples': [43206, 38698, 13388, 16836, 9118, 6466, 36818, 17521, 37347, 51652], 'chosen_samples_score': ['1.0222471', '1.0380341', '1.0496893', '1.0379', '1.0366008', '1.0357811', '1.050674', '1.0758368', '1.0592078', '1.1783187']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9602, 'nll': 0.3445750430226326}, 'chosen_samples': [52674, 31699, 58822, 45069, 52462, 13942, 52218, 1160, 52666, 29530], 'chosen_samples_score': ['1.0213988', '1.026859', '1.027236', '1.0274721', '1.068423', '1.0341113', '1.046876', '1.0474539', '1.0441228', '1.0462402']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9633, 'nll': 0.3259728983044624}, 'chosen_samples': [27292, 1376, 21023, 5315, 13030, 7924, 21636, 28723, 56006, 57822], 'chosen_samples_score': ['0.9989399', '1.0004698', '1.0020479', '1.0104432', '1.0728396', '1.0849371', '1.1240739', '1.0264668', '1.0433922', '1.0293502']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9635, 'nll': 0.3034121349453926}, 'chosen_samples': [57773, 12066, 966, 35401, 38656, 30605, 31293, 40654, 20169, 3367], 'chosen_samples_score': ['1.0126009', '1.021919', '1.0241524', '1.025918', '1.064196', '1.0972288', '1.0360415', '1.1026108', '1.0586392', '1.0903685']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9632, 'nll': 0.35285934060811996}, 'chosen_samples': [20869, 34847, 16150, 26850, 8178, 31650, 8117, 24479, 31748, 8879], 'chosen_samples_score': ['1.0907362', '1.1012378', '1.1029216', '1.1052799', '1.1652743', '1.1399696', '1.2170491', '1.1269653', '1.1198127', '1.1506299']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9656, 'nll': 0.3104605212807655}, 'chosen_samples': [48382, 39031, 5684, 39673, 4646, 42503, 39208, 15629, 32393, 40766], 'chosen_samples_score': ['0.9934612', '0.996711', '1.0073602', '1.2036504', '1.0797367', '1.0875256', '1.1285338', '1.0999926', '1.0734236', '1.0115689']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9668, 'nll': 0.2996903985738754}, 'chosen_samples': [9433, 21880, 12089, 15781, 7458, 21700, 41612, 55052, 14749, 49278], 'chosen_samples_score': ['0.97345936', '0.97769016', '0.981105', '0.9936035', '0.9877043', '0.9948276', '1.0414587', '1.0618184', '1.006862', '1.0107089']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9646, 'nll': 0.3252717092633247}, 'chosen_samples': [41744, 47432, 36450, 38219, 50236, 42787, 58832, 2381, 9390, 32776], 'chosen_samples_score': ['0.9913091', '0.99215823', '1.0011563', '1.0157728', '1.0133252', '1.0030242', '1.0289615', '1.1478101', '1.0751929', '1.2468214']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9669, 'nll': 0.29415093958377836}, 'chosen_samples': [30692, 32173, 59701, 3738, 16446, 8480, 32968, 4834, 20110, 1032], 'chosen_samples_score': ['1.0242953', '1.0256474', '1.1088283', '1.1314653', '1.0280961', '1.0372624', '1.0793719', '1.0415163', '1.026979', '1.0487878']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9699, 'nll': 0.27827661782503127}, 'chosen_samples': [8772, 27514, 15381, 17958, 47220, 38524, 18398, 18324, 20641, 48521], 'chosen_samples_score': ['0.9623976', '0.9734125', '0.98624873', '0.99882257', '1.0007434', '1.0051337', '1.0186925', '1.0425274', '1.0720699', '1.0195332']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9675, 'nll': 0.2870791807770729}, 'chosen_samples': [35643, 10746, 15494, 42199, 57728, 41999, 1674, 5842, 13969, 4822], 'chosen_samples_score': ['0.96159744', '0.9855827', '1.0030823', '1.0161493', '1.0360141', '1.0586625', '1.0391734', '1.0602176', '1.0831847', '1.076677']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9737, 'nll': 0.26503212153911593}, 'chosen_samples': [11074, 3692, 42472, 18487, 55190, 19362, 33062, 30915, 48102, 5065], 'chosen_samples_score': ['1.0377895', '1.0397737', '1.0463902', '1.0572056', '1.0934542', '1.0705886', '1.0605857', '1.1291263', '1.0630727', '1.068754']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9698, 'nll': 0.31808831840753554}, 'chosen_samples': [6289, 43897, 47328, 20002, 14586, 43950, 53872, 16070, 52169, 5790], 'chosen_samples_score': ['1.006986', '1.0080347', '1.0090888', '1.024395', '1.0210642', '1.0276833', '1.1135974', '1.158753', '1.1039542', '1.0793492']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9717, 'nll': 0.2792082905769348}, 'chosen_samples': [1137, 27429, 34908, 14722, 52306, 32426, 26379, 10982, 5295, 39575], 'chosen_samples_score': ['0.98878425', '0.99120516', '1.007901', '1.0163294', '1.0311201', '1.0672234', '1.0091579', '1.0148302', '1.1398283', '1.0221007']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9701, 'nll': 0.2707877948880196}, 'chosen_samples': [54896, 25945, 50086, 20820, 3644, 41924, 262, 9396, 37062, 45784], 'chosen_samples_score': ['0.9069508', '0.9094756', '0.9100918', '0.91420007', '0.92728025', '0.9880539', '0.94572484', '0.9499218', '0.94282305', '0.9515236']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9706, 'nll': 0.2808816909790039}, 'chosen_samples': [26635, 52690, 35326, 22053, 19138, 43256, 45602, 19404, 46088, 46878], 'chosen_samples_score': ['0.9383951', '0.9420382', '0.9540985', '0.9621805', '0.9569865', '0.9756825', '1.0307457', '0.97811544', '1.0134703', '1.0587182']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9742, 'nll': 0.2807823896408081}, 'chosen_samples': [35494, 14333, 13831, 59400, 44172, 49525, 33150, 27358, 41453, 50370], 'chosen_samples_score': ['0.98745203', '0.9905538', '1.0028303', '1.0012555', '1.0054193', '1.0380142', '1.1148896', '1.0208013', '1.024344', '1.0800183']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9733, 'nll': 0.27093741446733477}, 'chosen_samples': [45121, 49890, 33752, 26722, 55906, 12305, 12778, 34920, 37672, 3470], 'chosen_samples_score': ['0.8799944', '0.88855875', '0.90574026', '0.90673184', '0.8949111', '0.9341278', '0.9000352', '0.9419525', '1.0148244', '0.958389']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9723, 'nll': 0.2969263195991516}, 'chosen_samples': [59427, 30383, 20746, 21150, 14619, 43198, 41334, 6431, 54885, 36268], 'chosen_samples_score': ['0.8470913', '0.8502171', '0.8517696', '0.8567684', '0.85854024', '0.8764978', '0.9053274', '0.8915273', '0.9088461', '0.8770335']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9743, 'nll': 0.27840518206357956}, 'chosen_samples': [29119, 32108, 52691, 44756, 41713, 5302, 49514, 33576, 2148, 32276], 'chosen_samples_score': ['0.9633578', '0.96881235', '0.9685113', '0.970822', '0.97262776', '0.98752093', '0.9805229', '1.0089948', '1.1554943', '1.2242327']})
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store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9837, 'nll': 0.19370112642645837}, 'chosen_samples': [7270, 46432, 250, 37441, 38866, 3762, 59653, 30521, 14697, 1554], 'chosen_samples_score': ['0.8506471', '0.8524975', '0.8596436', '0.85651726', '0.8536384', '0.85967636', '0.9151524', '0.908702', '0.87360513', '0.8932621']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9839, 'nll': 0.1919518306851387}, 'chosen_samples': [38598, 37551, 49501, 49012, 8680, 30844, 52694, 27964, 14664, 17365], 'chosen_samples_score': ['0.8205389', '0.830305', '0.834256', '0.845178', '0.9134531', '0.9779268', '0.9832556', '0.8835343', '0.8822944', '0.9106951']})
store['iterations'].append({'num_epochs': 27, 'test_metrics': {'accuracy': 0.9828, 'nll': 0.19965266585350036}, 'chosen_samples': [7638, 18904, 892, 6102, 7184, 54181, 15386, 30688, 30658, 12651], 'chosen_samples_score': ['0.8664106', '0.8671075', '0.92291164', '0.8805439', '0.8806767', '0.925141', '0.90183467', '0.894252', '0.93052644', '1.1199548']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9843, 'nll': 0.18788893893361092}, 'chosen_samples': [29749, 31710, 31530, 36363, 48360, 41267, 35632, 52210, 28512, 34546], 'chosen_samples_score': ['0.82035655', '0.8215948', '0.85771626', '0.8475324', '0.85338515', '0.859131', '0.9964857', '0.8700053', '0.94893974', '0.8830873']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.985, 'nll': 0.16873459070920943}, 'chosen_samples': [43560, 14650, 29046, 57523, 22136, 16011, 10412, 27706, 11853, 53148], 'chosen_samples_score': ['0.8262116', '0.82957923', '0.8749265', '0.8466047', '0.84382606', '0.8543511', '0.924957', '0.96307623', '0.9590991', '0.962876']})
